AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Golgin subfamily A member 2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better activity, selectivity, and safety.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

We use our state-of-the-art dedicated workflow for designing focused libraries.

 Fig. 1. The sreening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

Q08379

UPID:

GOGA2_HUMAN

Alternative names:

130 kDa cis-Golgi matrix protein; GM130 autoantigen; Golgin-95

Alternative UPACC:

Q08379; A0A0C4DGS5; Q6GRM9; Q9BRB0; Q9NYF9

Background:

Golgin subfamily A member 2, also known as GM130, is a pivotal peripheral membrane component of the cis-Golgi stack. It plays a crucial role in maintaining the Golgi apparatus's structure, facilitating vesicle fusion, and ensuring normal protein transport from the endoplasmic reticulum. GM130 is instrumental in mitotic Golgi disassembly, spindle pole assembly, and centrosome organization, highlighting its central role in cell division and structural integrity.

Therapeutic significance:

The association of GM130 with developmental delay, hypotonia, myopathy, and brain abnormalities underscores its potential as a therapeutic target. Understanding GM130's role could open doors to novel strategies for treating these neurodevelopmental disorders.

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